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Build Interactive Models With R Shiny!

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Everything is going to be connected to cloud and data… The post Build Interactive Models With R Shiny! appeared first on Analytics Vidhya.

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Interactive Widget-Based Hyperparameter Tuning and Tracking in Pywedge

Analytics Vidhya

ArticleVideos This article was published as an entry for the Data Science Blogathon. Introduction Machine Learning is an iterative process and the Model building. The post Interactive Widget-Based Hyperparameter Tuning and Tracking in Pywedge appeared first on Analytics Vidhya.

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Fueling Enterprise Generative AI with Data: The Cornerstone of Differentiation

Cloudera

More than two-thirds of companies are currently using Generative AI (GenAI) models, such as large language models (LLMs), which can understand and generate human-like text, images, video, music, and even code. However, the true power of these models lies in their ability to adapt to an enterprise’s unique context.

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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Your Generative AI LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers

DataKitchen

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure. In this context, multi-tool Data Journey observability becomes crucial.

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Build a decentralized semantic search engine on heterogeneous data stores using autonomous agents

AWS Big Data

Large language models (LLMs) such as Anthropic Claude and Amazon Titan have the potential to drive automation across various business processes by processing both structured and unstructured data. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0

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Building and Evaluating GenAI Knowledge Management Systems using Ollama, Trulens and Cloudera

Cloudera

In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes. The image above demonstrates a KMS built using the llama3 model from Meta.